PrePublish - YouTube script QA

Check a script for reused or inauthentic content risk

check_authenticity

Check a script against YouTube's inauthentic-content expectations: whether it reads as mass-produced, templated or repetitive, which signals fire, and what to change. Returns a score, a risk level, the firing signals with quotes, and remediation steps. Choose this when the user worries about reused content, AI-sounding scripts, or a channel that repeats a formula. This reports text-level signals only; it is not a monetisation decision and does not speak for YouTube.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`script_text`	Yes	The script text as the user wrote it. Paste it verbatim; do not rewrite, summarise or clean it first.
`video_title`	Yes	The planned title.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`notice`	Yes	The scope limit that accompanies every Prepublish result. Repeat it to the user rather than dropping it.
`authenticity`	Yes	The inauthentic-content read on this script.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 20, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "authenticity": {
+      "description": "The inauthentic-content read on this script.",
+      "properties": {
+        "analysis_id": {
+          "description": "Prepublish id for this check.",
+          "type": "string"
+        },
+        "created_at": {
+          "description": "When the check was produced.",
+          "type": "string"
+        },
+        "heuristic_report": {
+          "description": "The deterministic layer behind the score, passed through as the backend produced it."
+        },
+        "override_note": {
+          "description": "Present when the banded result was overridden, explaining why.",
+          "type": "string"
+        },
+        "remediation": {
+          "description": "What to change, each with the current passage, the fix, and why it matters.",
+          "items": {
+            "type": "object"
+          },
+          "type": "array"
+        },
+        "risk_level": {
+          "description": "Banded risk level behind the score.",
+          "type": "string"
+        },
+        "score": {
+          "description": "Authenticity score for the script.",
+          "type": "integer"
+        },
+        "signals": {
+          "description": "Each signal that fired, with its name, severity, reasoning and the quote that triggered it.",
+          "items": {
+            "type": "object"
+          },
+          "type": "array"
+        },
+        "verdict": {
+          "description": "The short verdict in words.",
+          "type": "string"
+        }
+      },
+      "type": "object"
+    },
+    "notice": {
+      "description": "The scope limit that accompanies every Prepublish result. Repeat it to the user rather than dropping it.",
+      "type": "string"
+    }
+  },
+  "required": [
+    "notice",
+    "authenticity"
+  ],
+  "type": "object"
+}

First observedSep 10, 2026

TDQS

A4.3/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations (readOnlyHint=false, openWorldHint=false, destructiveHint=false) leave the agent unclear whether the call persists anything, and the description does not resolve that — it only says what is returned. It does add real behavioral context the annotations cannot: the report is text-level only, is not a monetisation decision, and does not speak for YouTube.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, then output shape, then selection criteria and caveats. Every sentence carries weight and there is no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and 100% schema coverage, the description only needs to convey purpose, scope boundaries and when to select it — all of which it does, including the caveat that it is not a monetisation ruling. Nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and both parameters are documented there, including the important 'paste verbatim; do not rewrite, summarise or clean it first' instruction. The description adds no further parameter meaning, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Check a script against YouTube's inauthentic-content expectations') and immediately narrows the scope to text-level signals, explicitly ruling out monetisation decisions. That boundary distinguishes it from sibling audit/policy tools like policy_preflight without needing to name them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit trigger conditions: 'Choose this when the user worries about reused content, AI-sounding scripts, or a channel that repeats a formula.' It also implicitly excludes monetisation questions, but never names a concrete alternative tool to use instead, so it stops short of full when/when-not routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All PrePublish - YouTube script QA tools Inspect

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